When using neural networks to detect features in an image, how can locate that specific feature in the original image?
This problem is called object detection.

If you have a trainings set of images with boxed objects you can just train a neural network to directly predict the box. I.e. the output has the same dimension as the input and the NN learns to assign each pixel the probability of belonging to a certain object.

If you don't have such a convenient dataset you could just recursively narrow the location down by feeding parts of the image to the network until you find the smallest part that still fully activates a certain classification. 

In this paper they try a mixture of these two approaches.